Overview

Dataset statistics

Number of variables35
Number of observations4424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory280.0 B

Variable types

Categorical12
Numeric17
Boolean6

Alerts

Application Mode is highly overall correlated with International StudentHigh correlation
Approved Units (1st Sem) is highly overall correlated with Approved Units (2nd Sem) and 4 other fieldsHigh correlation
Approved Units (2nd Sem) is highly overall correlated with Approved Units (1st Sem) and 5 other fieldsHigh correlation
Average Grade (1st Sem) is highly overall correlated with Approved Units (1st Sem) and 2 other fieldsHigh correlation
Average Grade (2nd Sem) is highly overall correlated with Approved Units (1st Sem) and 2 other fieldsHigh correlation
Course Name is highly overall correlated with Daytime/Evening AttendanceHigh correlation
Credited Units (1st Sem) is highly overall correlated with Credited Units (2nd Sem)High correlation
Credited Units (2nd Sem) is highly overall correlated with Credited Units (1st Sem)High correlation
Daytime/Evening Attendance is highly overall correlated with Course NameHigh correlation
Enrolled Units (1st Sem) is highly overall correlated with Approved Units (1st Sem) and 2 other fieldsHigh correlation
Enrolled Units (2nd Sem) is highly overall correlated with Approved Units (1st Sem) and 2 other fieldsHigh correlation
Evaluated Units (1st Sem) is highly overall correlated with Evaluated Units (2nd Sem)High correlation
Evaluated Units (2nd Sem) is highly overall correlated with Evaluated Units (1st Sem)High correlation
Father's Occupation is highly overall correlated with Mother's OccupationHigh correlation
International Student is highly overall correlated with Application Mode and 1 other fieldsHigh correlation
Mother's Occupation is highly overall correlated with Father's OccupationHigh correlation
Nationality is highly overall correlated with International StudentHigh correlation
Student Status is highly overall correlated with Approved Units (2nd Sem)High correlation
Marital Status is highly imbalanced (75.3%)Imbalance
Daytime/Evening Attendance is highly imbalanced (50.3%)Imbalance
Previous Qualification is highly imbalanced (73.4%)Imbalance
Nationality is highly imbalanced (94.3%)Imbalance
Special Educational Needs is highly imbalanced (90.9%)Imbalance
International Student is highly imbalanced (83.2%)Imbalance
Credited Units (1st Sem) has 3847 (87.0%) zerosZeros
Enrolled Units (1st Sem) has 180 (4.1%) zerosZeros
Evaluated Units (1st Sem) has 349 (7.9%) zerosZeros
Approved Units (1st Sem) has 718 (16.2%) zerosZeros
Average Grade (1st Sem) has 718 (16.2%) zerosZeros
Not Evaluated Units (1st Sem) has 4130 (93.4%) zerosZeros
Credited Units (2nd Sem) has 3894 (88.0%) zerosZeros
Enrolled Units (2nd Sem) has 180 (4.1%) zerosZeros
Evaluated Units (2nd Sem) has 401 (9.1%) zerosZeros
Approved Units (2nd Sem) has 870 (19.7%) zerosZeros
Average Grade (2nd Sem) has 870 (19.7%) zerosZeros
Not Evaluated Units (2nd Sem) has 4142 (93.6%) zerosZeros

Reproduction

Analysis started2025-12-11 09:17:44.002526
Analysis finished2025-12-11 09:18:04.783215
Duration20.78 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Marital Status
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Single
3919 
Married
 
379
Divorced
 
91
Facto Union
 
25
Legally Separated
 
6

Length

Max length17
Median length6
Mean length6.1708861
Min length6

Characters and Unicode

Total characters27300
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Single3919
88.6%
Married379
 
8.6%
Divorced91
 
2.1%
Facto Union25
 
0.6%
Legally Separated6
 
0.1%
Widower4
 
0.1%

Length

2025-12-11T14:48:05.121240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:48:05.170180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single3919
88.0%
married379
 
8.5%
divorced91
 
2.0%
facto25
 
0.6%
union25
 
0.6%
legally6
 
0.1%
separated6
 
0.1%
widower4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i4418
16.2%
e4411
16.2%
n3969
14.5%
l3931
14.4%
S3925
14.4%
g3925
14.4%
r859
 
3.1%
d480
 
1.8%
a422
 
1.5%
M379
 
1.4%
Other values (13)581
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)27300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i4418
16.2%
e4411
16.2%
n3969
14.5%
l3931
14.4%
S3925
14.4%
g3925
14.4%
r859
 
3.1%
d480
 
1.8%
a422
 
1.5%
M379
 
1.4%
Other values (13)581
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i4418
16.2%
e4411
16.2%
n3969
14.5%
l3931
14.4%
S3925
14.4%
g3925
14.4%
r859
 
3.1%
d480
 
1.8%
a422
 
1.5%
M379
 
1.4%
Other values (13)581
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i4418
16.2%
e4411
16.2%
n3969
14.5%
l3931
14.4%
S3925
14.4%
g3925
14.4%
r859
 
3.1%
d480
 
1.8%
a422
 
1.5%
M379
 
1.4%
Other values (13)581
 
2.1%

Application Mode
Categorical

High correlation 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1st phase—general contingent
1708 
2nd phase—general contingent
872 
Over 23 years old
785 
Change in course
312 
Technological specialization diploma holders
213 
Other values (13)
534 

Length

Max length51
Median length28
Mean length25.936031
Min length8

Characters and Unicode

Total characters114741
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row2nd phase—general contingent
2nd rowInternational student (bachelor)
3rd row1st phase—general contingent
4th row2nd phase—general contingent
5th rowOver 23 years old

Common Values

ValueCountFrequency (%)
1st phase—general contingent1708
38.6%
2nd phase—general contingent872
19.7%
Over 23 years old785
17.7%
Change in course312
 
7.1%
Technological specialization diploma holders213
 
4.8%
Holders of other higher courses139
 
3.1%
3rd phase—general contingent124
 
2.8%
Transfer77
 
1.7%
Change in institution/course59
 
1.3%
1st phase—special contingent (Madeira Island)38
 
0.9%
Other values (8)97
 
2.2%

Length

2025-12-11T14:48:05.232766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
contingent2758
19.0%
phase—general2704
18.6%
1st1762
12.1%
2nd872
 
6.0%
over785
 
5.4%
23785
 
5.4%
years785
 
5.4%
old785
 
5.4%
holders387
 
2.7%
change372
 
2.6%
Other values (32)2551
17.5%

Most occurring characters

ValueCountFrequency (%)
e14799
12.9%
n13428
11.7%
10122
 
8.8%
t7974
 
6.9%
a7875
 
6.9%
s6847
 
6.0%
g6186
 
5.4%
r5895
 
5.1%
o5794
 
5.0%
l4968
 
4.3%
Other values (39)30853
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)114741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e14799
12.9%
n13428
11.7%
10122
 
8.8%
t7974
 
6.9%
a7875
 
6.9%
s6847
 
6.0%
g6186
 
5.4%
r5895
 
5.1%
o5794
 
5.0%
l4968
 
4.3%
Other values (39)30853
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)114741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e14799
12.9%
n13428
11.7%
10122
 
8.8%
t7974
 
6.9%
a7875
 
6.9%
s6847
 
6.0%
g6186
 
5.4%
r5895
 
5.1%
o5794
 
5.0%
l4968
 
4.3%
Other values (39)30853
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)114741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e14799
12.9%
n13428
11.7%
10122
 
8.8%
t7974
 
6.9%
a7875
 
6.9%
s6847
 
6.0%
g6186
 
5.4%
r5895
 
5.1%
o5794
 
5.0%
l4968
 
4.3%
Other values (39)30853
26.9%

Application Order
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7278481
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:05.277107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3137931
Coefficient of variation (CV)0.76036376
Kurtosis2.6512887
Mean1.7278481
Median Absolute Deviation (MAD)0
Skewness1.88105
Sum7644
Variance1.7260523
MonotonicityNot monotonic
2025-12-11T14:48:05.324166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13026
68.4%
2547
 
12.4%
3309
 
7.0%
4249
 
5.6%
5154
 
3.5%
6137
 
3.1%
91
 
< 0.1%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
13026
68.4%
2547
 
12.4%
3309
 
7.0%
4249
 
5.6%
5154
 
3.5%
6137
 
3.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
6137
 
3.1%
5154
 
3.5%
4249
 
5.6%
3309
 
7.0%
2547
 
12.4%
13026
68.4%
01
 
< 0.1%

Course Name
Categorical

High correlation 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Nursing
766 
Management
380 
Social Service
355 
Veterinary Nursing
337 
Journalism and Communication
331 
Other values (12)
2255 

Length

Max length36
Median length28
Mean length17.993445
Min length7

Characters and Unicode

Total characters79603
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimation and Multimedia Design
2nd rowTourism
3rd rowCommunication Design
4th rowJournalism and Communication
5th rowSocial Service (evening attendance)

Common Values

ValueCountFrequency (%)
Nursing766
17.3%
Management380
 
8.6%
Social Service355
 
8.0%
Veterinary Nursing337
 
7.6%
Journalism and Communication331
 
7.5%
Advertising and Marketing Management268
 
6.1%
Management (evening attendance)268
 
6.1%
Tourism252
 
5.7%
Communication Design226
 
5.1%
Animation and Multimedia Design215
 
4.9%
Other values (7)1026
23.2%

Length

2025-12-11T14:48:05.381679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nursing1103
 
12.1%
management916
 
10.1%
and814
 
8.9%
social570
 
6.3%
service570
 
6.3%
communication557
 
6.1%
evening483
 
5.3%
attendance483
 
5.3%
design441
 
4.8%
veterinary337
 
3.7%
Other values (17)2832
31.1%

Most occurring characters

ValueCountFrequency (%)
n10203
12.8%
i8163
 
10.3%
e7459
 
9.4%
a6745
 
8.5%
4682
 
5.9%
t4257
 
5.3%
r4255
 
5.3%
g4127
 
5.2%
m3423
 
4.3%
o3324
 
4.2%
Other values (28)22965
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)79603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n10203
12.8%
i8163
 
10.3%
e7459
 
9.4%
a6745
 
8.5%
4682
 
5.9%
t4257
 
5.3%
r4255
 
5.3%
g4127
 
5.2%
m3423
 
4.3%
o3324
 
4.2%
Other values (28)22965
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)79603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n10203
12.8%
i8163
 
10.3%
e7459
 
9.4%
a6745
 
8.5%
4682
 
5.9%
t4257
 
5.3%
r4255
 
5.3%
g4127
 
5.2%
m3423
 
4.3%
o3324
 
4.2%
Other values (28)22965
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)79603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n10203
12.8%
i8163
 
10.3%
e7459
 
9.4%
a6745
 
8.5%
4682
 
5.9%
t4257
 
5.3%
r4255
 
5.3%
g4127
 
5.2%
m3423
 
4.3%
o3324
 
4.2%
Other values (28)22965
28.8%

Daytime/Evening Attendance
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Daytime
3941 
Evening
483 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters30968
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaytime
2nd rowDaytime
3rd rowDaytime
4th rowDaytime
5th rowEvening

Common Values

ValueCountFrequency (%)
Daytime3941
89.1%
Evening483
 
10.9%

Length

2025-12-11T14:48:05.435493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:48:05.470225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
daytime3941
89.1%
evening483
 
10.9%

Most occurring characters

ValueCountFrequency (%)
i4424
14.3%
e4424
14.3%
D3941
12.7%
y3941
12.7%
a3941
12.7%
t3941
12.7%
m3941
12.7%
n966
 
3.1%
E483
 
1.6%
v483
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)30968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i4424
14.3%
e4424
14.3%
D3941
12.7%
y3941
12.7%
a3941
12.7%
t3941
12.7%
m3941
12.7%
n966
 
3.1%
E483
 
1.6%
v483
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i4424
14.3%
e4424
14.3%
D3941
12.7%
y3941
12.7%
a3941
12.7%
t3941
12.7%
m3941
12.7%
n966
 
3.1%
E483
 
1.6%
v483
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i4424
14.3%
e4424
14.3%
D3941
12.7%
y3941
12.7%
a3941
12.7%
t3941
12.7%
m3941
12.7%
n966
 
3.1%
E483
 
1.6%
v483
 
1.6%

Previous Qualification
Categorical

Imbalance 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Secondary education
3717 
Technological specialization course
 
219
Basic education 3rd cycle
 
162
Higher education—degree
 
126
Other—11th year
 
45
Other values (12)
 
155

Length

Max length37
Median length19
Mean length20.55425
Min length9

Characters and Unicode

Total characters90932
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSecondary education
2nd rowSecondary education
3rd rowSecondary education
4th rowSecondary education
5th rowSecondary education

Common Values

ValueCountFrequency (%)
Secondary education3717
84.0%
Technological specialization course219
 
5.0%
Basic education 3rd cycle162
 
3.7%
Higher education—degree126
 
2.8%
Other—11th year45
 
1.0%
Higher education—degree (1st cycle)40
 
0.9%
Professional higher technical course36
 
0.8%
Higher education—bachelor’s degree23
 
0.5%
Frequency of higher education16
 
0.4%
12th year—not completed11
 
0.2%
Other values (7)29
 
0.7%

Length

2025-12-11T14:48:05.522267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education3902
40.4%
secondary3717
38.5%
higher256
 
2.7%
course255
 
2.6%
technological219
 
2.3%
specialization219
 
2.3%
cycle215
 
2.2%
basic169
 
1.8%
education—degree166
 
1.7%
3rd162
 
1.7%
Other values (17)369
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e9862
10.8%
c9463
10.4%
o8882
9.8%
a8822
9.7%
n8379
9.2%
d8213
9.0%
i5479
 
6.0%
5225
 
5.7%
r4785
 
5.3%
t4559
 
5.0%
Other values (27)17263
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)90932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9862
10.8%
c9463
10.4%
o8882
9.8%
a8822
9.7%
n8379
9.2%
d8213
9.0%
i5479
 
6.0%
5225
 
5.7%
r4785
 
5.3%
t4559
 
5.0%
Other values (27)17263
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)90932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9862
10.8%
c9463
10.4%
o8882
9.8%
a8822
9.7%
n8379
9.2%
d8213
9.0%
i5479
 
6.0%
5225
 
5.7%
r4785
 
5.3%
t4559
 
5.0%
Other values (27)17263
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)90932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9862
10.8%
c9463
10.4%
o8882
9.8%
a8822
9.7%
n8379
9.2%
d8213
9.0%
i5479
 
6.0%
5225
 
5.7%
r4785
 
5.3%
t4559
 
5.0%
Other values (27)17263
19.0%

Nationality
Categorical

High correlation  Imbalance 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Portuguese
4314 
Brazilian
 
38
Santomean
 
14
Spanish
 
13
Cape Verdean
 
13
Other values (16)
 
32

Length

Max length12
Median length10
Mean length9.9672242
Min length5

Characters and Unicode

Total characters44095
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowPortuguese
2nd rowPortuguese
3rd rowPortuguese
4th rowPortuguese
5th rowPortuguese

Common Values

ValueCountFrequency (%)
Portuguese4314
97.5%
Brazilian38
 
0.9%
Santomean14
 
0.3%
Spanish13
 
0.3%
Cape Verdean13
 
0.3%
Guinean5
 
0.1%
Moldovan3
 
0.1%
Italian3
 
0.1%
Ukrainian3
 
0.1%
Romanian2
 
< 0.1%
Other values (11)16
 
0.4%

Length

2025-12-11T14:48:05.580159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
portuguese4314
97.2%
brazilian38
 
0.9%
santomean14
 
0.3%
spanish13
 
0.3%
cape13
 
0.3%
verdean13
 
0.3%
guinean5
 
0.1%
moldovan3
 
0.1%
italian3
 
0.1%
ukrainian3
 
0.1%
Other values (12)18
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e8690
19.7%
u8639
19.6%
r4371
9.9%
o4342
9.8%
t4333
9.8%
s4333
9.8%
g4317
9.8%
P4314
9.8%
a183
 
0.4%
n135
 
0.3%
Other values (27)438
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)44095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8690
19.7%
u8639
19.6%
r4371
9.9%
o4342
9.8%
t4333
9.8%
s4333
9.8%
g4317
9.8%
P4314
9.8%
a183
 
0.4%
n135
 
0.3%
Other values (27)438
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8690
19.7%
u8639
19.6%
r4371
9.9%
o4342
9.8%
t4333
9.8%
s4333
9.8%
g4317
9.8%
P4314
9.8%
a183
 
0.4%
n135
 
0.3%
Other values (27)438
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8690
19.7%
u8639
19.6%
r4371
9.9%
o4342
9.8%
t4333
9.8%
s4333
9.8%
g4317
9.8%
P4314
9.8%
a183
 
0.4%
n135
 
0.3%
Other values (27)438
 
1.0%
Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Secondary Education
1069 
Administration & Commerce course
1009 
General commerce course
953 
Accounting & Admin course
562 
Higher Education Degree
438 
Other values (24)
393 

Length

Max length32
Median length31
Mean length24.10443
Min length7

Characters and Unicode

Total characters106638
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowGeneral commerce course
2nd rowSecondary Education
3rd rowAdministration & Commerce course
4th rowAccounting & Admin course
5th rowAdministration & Commerce course

Common Values

ValueCountFrequency (%)
Secondary Education1069
24.2%
Administration & Commerce course1009
22.8%
General commerce course953
21.5%
Accounting & Admin course562
12.7%
Higher Education Degree438
9.9%
2nd cycle general high school130
 
2.9%
Bachelor’s Degree83
 
1.9%
Master’s Degree49
 
1.1%
Other—11th Year42
 
0.9%
Doctorate21
 
0.5%
Other values (19)68
 
1.5%

Length

2025-12-11T14:48:05.645359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
course2527
18.3%
commerce1962
14.2%
1571
11.4%
education1512
10.9%
general1083
7.8%
secondary1069
7.7%
administration1009
 
7.3%
degree570
 
4.1%
accounting562
 
4.1%
admin562
 
4.1%
Other values (39)1384
10.0%

Most occurring characters

ValueCountFrequency (%)
e12316
11.5%
c9691
 
9.1%
9387
 
8.8%
o9104
 
8.5%
r8961
 
8.4%
n7588
 
7.1%
i6262
 
5.9%
m5518
 
5.2%
a4943
 
4.6%
u4616
 
4.3%
Other values (43)28252
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)106638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e12316
11.5%
c9691
 
9.1%
9387
 
8.8%
o9104
 
8.5%
r8961
 
8.4%
n7588
 
7.1%
i6262
 
5.9%
m5518
 
5.2%
a4943
 
4.6%
u4616
 
4.3%
Other values (43)28252
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)106638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e12316
11.5%
c9691
 
9.1%
9387
 
8.8%
o9104
 
8.5%
r8961
 
8.4%
n7588
 
7.1%
i6262
 
5.9%
m5518
 
5.2%
a4943
 
4.6%
u4616
 
4.3%
Other values (43)28252
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)106638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e12316
11.5%
c9691
 
9.1%
9387
 
8.8%
o9104
 
8.5%
r8961
 
8.4%
n7588
 
7.1%
i6262
 
5.9%
m5518
 
5.2%
a4943
 
4.6%
u4616
 
4.3%
Other values (43)28252
26.5%
Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Basic Edu 1st Cycle
1209 
Basic Education 3rd Cycle
968 
Secondary Education
904 
Basic Edu 2nd Cycle
702 
Higher Education Degree
282 
Other values (29)
359 

Length

Max length32
Median length19
Mean length20.173599
Min length7

Characters and Unicode

Total characters89248
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowOther—11th Year
2nd rowHigher Education Degree
3rd rowBasic Edu 1st Cycle
4th rowBasic Edu 1st Cycle
5th rowBasic Edu 2nd Cycle

Common Values

ValueCountFrequency (%)
Basic Edu 1st Cycle1209
27.3%
Basic Education 3rd Cycle968
21.9%
Secondary Education904
20.4%
Basic Edu 2nd Cycle702
15.9%
Higher Education Degree282
 
6.4%
Unknown112
 
2.5%
Bachelor’s Degree68
 
1.5%
Master’s Degree39
 
0.9%
Other—11th Year38
 
0.9%
Tech Specialization Course20
 
0.5%
Other values (24)82
 
1.9%

Length

2025-12-11T14:48:05.704513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cycle2888
19.4%
basic2879
19.4%
education2156
14.5%
edu1916
12.9%
1st1214
8.2%
3rd969
 
6.5%
secondary904
 
6.1%
2nd706
 
4.8%
degree394
 
2.7%
higher292
 
2.0%
Other values (43)540
 
3.6%

Most occurring characters

ValueCountFrequency (%)
10434
 
11.7%
c9005
 
10.1%
d6689
 
7.5%
a6217
 
7.0%
e5639
 
6.3%
i5412
 
6.1%
s4284
 
4.8%
n4174
 
4.7%
u4107
 
4.6%
E4072
 
4.6%
Other values (44)29215
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)89248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10434
 
11.7%
c9005
 
10.1%
d6689
 
7.5%
a6217
 
7.0%
e5639
 
6.3%
i5412
 
6.1%
s4284
 
4.8%
n4174
 
4.7%
u4107
 
4.6%
E4072
 
4.6%
Other values (44)29215
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)89248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10434
 
11.7%
c9005
 
10.1%
d6689
 
7.5%
a6217
 
7.0%
e5639
 
6.3%
i5412
 
6.1%
s4284
 
4.8%
n4174
 
4.7%
u4107
 
4.6%
E4072
 
4.6%
Other values (44)29215
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)89248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10434
 
11.7%
c9005
 
10.1%
d6689
 
7.5%
a6217
 
7.0%
e5639
 
6.3%
i5412
 
6.1%
s4284
 
4.8%
n4174
 
4.7%
u4107
 
4.6%
E4072
 
4.6%
Other values (44)29215
32.7%

Mother's Occupation
Categorical

High correlation 

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Unskilled Workers
1577 
Administrative Staff
817 
Service/Sales/Security
530 
Technicians & Professionals
351 
Scientific Specialists
318 
Other values (27)
831 

Length

Max length32
Median length31
Mean length19.721745
Min length5

Characters and Unicode

Total characters87249
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowService/Sales/Security
2nd rowTechnicians & Professionals
3rd rowUnskilled Workers
4th rowService/Sales/Security
5th rowUnskilled Workers

Common Values

ValueCountFrequency (%)
Unskilled Workers1577
35.6%
Administrative Staff817
18.5%
Service/Sales/Security530
 
12.0%
Technicians & Professionals351
 
7.9%
Scientific Specialists318
 
7.2%
Skilled Industry Workers272
 
6.1%
Student144
 
3.3%
Legislative/Executive Officers102
 
2.3%
Farmers/Ag Workers91
 
2.1%
Other Situation70
 
1.6%
Other values (22)152
 
3.4%

Length

2025-12-11T14:48:05.777777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
workers1961
22.2%
unskilled1577
17.9%
administrative817
9.3%
staff817
9.3%
service/sales/security530
 
6.0%
technicians359
 
4.1%
professionals359
 
4.1%
351
 
4.0%
specialists328
 
3.7%
scientific318
 
3.6%
Other values (33)1405
15.9%

Most occurring characters

ValueCountFrequency (%)
e9175
 
10.5%
i8722
 
10.0%
s7640
 
8.8%
r7004
 
8.0%
l5067
 
5.8%
t4730
 
5.4%
n4411
 
5.1%
4398
 
5.0%
k3827
 
4.4%
a3631
 
4.2%
Other values (31)28644
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)87249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9175
 
10.5%
i8722
 
10.0%
s7640
 
8.8%
r7004
 
8.0%
l5067
 
5.8%
t4730
 
5.4%
n4411
 
5.1%
4398
 
5.0%
k3827
 
4.4%
a3631
 
4.2%
Other values (31)28644
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)87249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9175
 
10.5%
i8722
 
10.0%
s7640
 
8.8%
r7004
 
8.0%
l5067
 
5.8%
t4730
 
5.4%
n4411
 
5.1%
4398
 
5.0%
k3827
 
4.4%
a3631
 
4.2%
Other values (31)28644
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)87249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9175
 
10.5%
i8722
 
10.0%
s7640
 
8.8%
r7004
 
8.0%
l5067
 
5.8%
t4730
 
5.4%
n4411
 
5.1%
4398
 
5.0%
k3827
 
4.4%
a3631
 
4.2%
Other values (31)28644
32.8%

Father's Occupation
Categorical

High correlation 

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Unskilled Workers
1010 
Skilled Industry Workers
666 
Service/Sales/Security
516 
Administrative Staff
386 
Technicians & Professionals
384 
Other values (41)
1462 

Length

Max length32
Median length30
Mean length19.861212
Min length5

Characters and Unicode

Total characters87866
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.3%

Sample

1st rowUnskilled Workers
2nd rowTechnicians & Professionals
3rd rowUnskilled Workers
4th rowTechnicians & Professionals
5th rowUnskilled Workers

Common Values

ValueCountFrequency (%)
Unskilled Workers1010
22.8%
Skilled Industry Workers666
15.1%
Service/Sales/Security516
11.7%
Administrative Staff386
 
8.7%
Technicians & Professionals384
 
8.7%
Machine Operators318
 
7.2%
Armed Forces266
 
6.0%
Farmers/Ag Workers242
 
5.5%
Scientific Specialists197
 
4.5%
Legislative/Executive Officers134
 
3.0%
Other values (36)305
 
6.9%

Length

2025-12-11T14:48:05.854245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
workers1945
21.0%
unskilled1031
11.1%
industry668
 
7.2%
skilled666
 
7.2%
service/sales/security516
 
5.6%
technicians390
 
4.2%
administrative386
 
4.2%
staff386
 
4.2%
professionals386
 
4.2%
384
 
4.1%
Other values (53)2505
27.0%

Most occurring characters

ValueCountFrequency (%)
e9662
 
11.0%
r8374
 
9.5%
s7694
 
8.8%
i7091
 
8.1%
4839
 
5.5%
l4682
 
5.3%
n4065
 
4.6%
t3894
 
4.4%
k3662
 
4.2%
o3445
 
3.9%
Other values (34)30458
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)87866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9662
 
11.0%
r8374
 
9.5%
s7694
 
8.8%
i7091
 
8.1%
4839
 
5.5%
l4682
 
5.3%
n4065
 
4.6%
t3894
 
4.4%
k3662
 
4.2%
o3445
 
3.9%
Other values (34)30458
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)87866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9662
 
11.0%
r8374
 
9.5%
s7694
 
8.8%
i7091
 
8.1%
4839
 
5.5%
l4682
 
5.3%
n4065
 
4.6%
t3894
 
4.4%
k3662
 
4.2%
o3445
 
3.9%
Other values (34)30458
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)87866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9662
 
11.0%
r8374
 
9.5%
s7694
 
8.8%
i7091
 
8.1%
4839
 
5.5%
l4682
 
5.3%
n4065
 
4.6%
t3894
 
4.4%
k3662
 
4.2%
o3445
 
3.9%
Other values (34)30458
34.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
True
2426 
False
1998 
ValueCountFrequency (%)
True2426
54.8%
False1998
45.2%
2025-12-11T14:48:05.893582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Special Educational Needs
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
4373 
True
 
51
ValueCountFrequency (%)
False4373
98.8%
True51
 
1.2%
2025-12-11T14:48:05.920084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Is Debtor
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
3921 
True
503 
ValueCountFrequency (%)
False3921
88.6%
True503
 
11.4%
2025-12-11T14:48:05.943750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
True
3896 
False
528 
ValueCountFrequency (%)
True3896
88.1%
False528
 
11.9%
2025-12-11T14:48:05.968775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Female
2868 
Male
1556 

Length

Max length6
Median length6
Mean length5.2965642
Min length4

Characters and Unicode

Total characters23432
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female2868
64.8%
Male1556
35.2%

Length

2025-12-11T14:48:06.025100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:48:06.062221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female2868
64.8%
male1556
35.2%

Most occurring characters

ValueCountFrequency (%)
e7292
31.1%
a4424
18.9%
l4424
18.9%
F2868
 
12.2%
m2868
 
12.2%
M1556
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)23432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7292
31.1%
a4424
18.9%
l4424
18.9%
F2868
 
12.2%
m2868
 
12.2%
M1556
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7292
31.1%
a4424
18.9%
l4424
18.9%
F2868
 
12.2%
m2868
 
12.2%
M1556
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7292
31.1%
a4424
18.9%
l4424
18.9%
F2868
 
12.2%
m2868
 
12.2%
M1556
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
3325 
True
1099 
ValueCountFrequency (%)
False3325
75.2%
True1099
 
24.8%
2025-12-11T14:48:06.098009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Age at Enrollment
Real number (ℝ)

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.265145
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.145912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5878156
Coefficient of variation (CV)0.32614522
Kurtosis4.1268918
Mean23.265145
Median Absolute Deviation (MAD)2
Skewness2.0549884
Sum102925
Variance57.574946
MonotonicityNot monotonic
2025-12-11T14:48:06.211597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
181036
23.4%
19911
20.6%
20599
13.5%
21322
 
7.3%
22174
 
3.9%
24131
 
3.0%
23108
 
2.4%
2694
 
2.1%
2593
 
2.1%
2791
 
2.1%
Other values (36)865
19.6%
ValueCountFrequency (%)
175
 
0.1%
181036
23.4%
19911
20.6%
20599
13.5%
21322
 
7.3%
22174
 
3.9%
23108
 
2.4%
24131
 
3.0%
2593
 
2.1%
2694
 
2.1%
ValueCountFrequency (%)
701
 
< 0.1%
621
 
< 0.1%
611
 
< 0.1%
602
 
< 0.1%
593
0.1%
583
0.1%
572
 
< 0.1%
555
0.1%
547
0.2%
537
0.2%

International Student
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
4314 
True
 
110
ValueCountFrequency (%)
False4314
97.5%
True110
 
2.5%
2025-12-11T14:48:06.255476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Credited Units (1st Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70999096
Minimum0
Maximum20
Zeros3847
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.299802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3605066
Coefficient of variation (CV)3.3246995
Kurtosis19.205727
Mean0.70999096
Median Absolute Deviation (MAD)0
Skewness4.1690488
Sum3141
Variance5.5719915
MonotonicityNot monotonic
2025-12-11T14:48:06.352524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
03847
87.0%
294
 
2.1%
185
 
1.9%
369
 
1.6%
651
 
1.2%
447
 
1.1%
741
 
0.9%
541
 
0.9%
831
 
0.7%
927
 
0.6%
Other values (11)91
 
2.1%
ValueCountFrequency (%)
03847
87.0%
185
 
1.9%
294
 
2.1%
369
 
1.6%
447
 
1.1%
541
 
0.9%
651
 
1.2%
741
 
0.9%
831
 
0.7%
927
 
0.6%
ValueCountFrequency (%)
202
 
< 0.1%
192
 
< 0.1%
184
 
0.1%
173
 
0.1%
163
 
0.1%
155
 
0.1%
1415
0.3%
1313
0.3%
1212
0.3%
1117
0.4%

Enrolled Units (1st Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2705696
Minimum0
Maximum26
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.405161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4801782
Coefficient of variation (CV)0.39552677
Kurtosis8.9379154
Mean6.2705696
Median Absolute Deviation (MAD)1
Skewness1.6190409
Sum27741
Variance6.1512838
MonotonicityNot monotonic
2025-12-11T14:48:06.457588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61910
43.2%
51010
22.8%
7656
 
14.8%
8296
 
6.7%
0180
 
4.1%
1266
 
1.5%
1052
 
1.2%
1145
 
1.0%
936
 
0.8%
1525
 
0.6%
Other values (13)148
 
3.3%
ValueCountFrequency (%)
0180
 
4.1%
17
 
0.2%
29
 
0.2%
310
 
0.2%
421
 
0.5%
51010
22.8%
61910
43.2%
7656
 
14.8%
8296
 
6.7%
936
 
0.8%
ValueCountFrequency (%)
261
 
< 0.1%
232
 
< 0.1%
216
 
0.1%
192
 
< 0.1%
1819
0.4%
1716
0.4%
1613
0.3%
1525
0.6%
1422
0.5%
1320
0.5%

Evaluated Units (1st Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2990506
Minimum0
Maximum45
Zeros349
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.510979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1791056
Coefficient of variation (CV)0.50356429
Kurtosis5.4630252
Mean8.2990506
Median Absolute Deviation (MAD)2
Skewness0.9766367
Sum36715
Variance17.464923
MonotonicityNot monotonic
2025-12-11T14:48:06.567094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8791
17.9%
7703
15.9%
6598
13.5%
9402
9.1%
0349
7.9%
10340
7.7%
11239
 
5.4%
12223
 
5.0%
5220
 
5.0%
13140
 
3.2%
Other values (25)419
9.5%
ValueCountFrequency (%)
0349
7.9%
16
 
0.1%
28
 
0.2%
36
 
0.1%
419
 
0.4%
5220
 
5.0%
6598
13.5%
7703
15.9%
8791
17.9%
9402
9.1%
ValueCountFrequency (%)
452
< 0.1%
361
 
< 0.1%
331
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
292
< 0.1%
281
 
< 0.1%
272
< 0.1%
264
0.1%
253
0.1%

Approved Units (1st Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7066004
Minimum0
Maximum26
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.624913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.094238
Coefficient of variation (CV)0.65742526
Kurtosis3.0966799
Mean4.7066004
Median Absolute Deviation (MAD)1
Skewness0.7662624
Sum20822
Variance9.5743087
MonotonicityNot monotonic
2025-12-11T14:48:06.678860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61171
26.5%
5723
16.3%
0718
16.2%
7471
10.6%
4433
 
9.8%
3269
 
6.1%
2160
 
3.6%
1127
 
2.9%
8108
 
2.4%
1149
 
1.1%
Other values (13)195
 
4.4%
ValueCountFrequency (%)
0718
16.2%
1127
 
2.9%
2160
 
3.6%
3269
 
6.1%
4433
 
9.8%
5723
16.3%
61171
26.5%
7471
10.6%
8108
 
2.4%
940
 
0.9%
ValueCountFrequency (%)
261
 
< 0.1%
214
 
0.1%
203
 
0.1%
192
 
< 0.1%
1815
0.3%
1710
 
0.2%
165
 
0.1%
157
 
0.2%
1414
0.3%
1326
0.6%

Average Grade (1st Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct805
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.640822
Minimum0
Maximum18.875
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.737873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.285714
Q313.4
95-th percentile14.857143
Maximum18.875
Range18.875
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.8436634
Coefficient of variation (CV)0.45519637
Kurtosis0.90846103
Mean10.640822
Median Absolute Deviation (MAD)1.1571429
Skewness-1.5681456
Sum47074.995
Variance23.461075
MonotonicityNot monotonic
2025-12-11T14:48:06.806357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0718
 
16.2%
12205
 
4.6%
13147
 
3.3%
11138
 
3.1%
11.589
 
2.0%
1485
 
1.9%
12.584
 
1.9%
12.3333333382
 
1.9%
12.6666666782
 
1.9%
1082
 
1.9%
Other values (795)2712
61.3%
ValueCountFrequency (%)
0718
16.2%
9.81
 
< 0.1%
1082
 
1.9%
10.166666671
 
< 0.1%
10.28
 
0.2%
10.214285711
 
< 0.1%
10.257
 
0.2%
10.285714291
 
< 0.1%
10.3333333316
 
0.4%
10.368421051
 
< 0.1%
ValueCountFrequency (%)
18.8751
 
< 0.1%
182
 
< 0.1%
17.333333332
 
< 0.1%
17.1251
 
< 0.1%
17.111111111
 
< 0.1%
17.005555561
 
< 0.1%
175
0.1%
16.91
 
< 0.1%
16.885714291
 
< 0.1%
16.857142861
 
< 0.1%

Not Evaluated Units (1st Sem)
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13765823
Minimum0
Maximum12
Zeros4130
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.865764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69088018
Coefficient of variation (CV)5.0188078
Kurtosis89.863208
Mean0.13765823
Median Absolute Deviation (MAD)0
Skewness8.2074031
Sum609
Variance0.47731543
MonotonicityNot monotonic
2025-12-11T14:48:06.918931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
04130
93.4%
1153
 
3.5%
279
 
1.8%
323
 
0.5%
415
 
0.3%
66
 
0.1%
76
 
0.1%
55
 
0.1%
84
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
04130
93.4%
1153
 
3.5%
279
 
1.8%
323
 
0.5%
415
 
0.3%
55
 
0.1%
66
 
0.1%
76
 
0.1%
84
 
0.1%
101
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
101
 
< 0.1%
84
 
0.1%
76
 
0.1%
66
 
0.1%
55
 
0.1%
415
 
0.3%
323
 
0.5%
279
1.8%
1153
3.5%

Credited Units (2nd Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54181736
Minimum0
Maximum19
Zeros3894
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:06.965930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9185461
Coefficient of variation (CV)3.5409462
Kurtosis24.427266
Mean0.54181736
Median Absolute Deviation (MAD)0
Skewness4.6348195
Sum2397
Variance3.6808193
MonotonicityNot monotonic
2025-12-11T14:48:07.014772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
03894
88.0%
1107
 
2.4%
292
 
2.1%
478
 
1.8%
568
 
1.5%
349
 
1.1%
626
 
0.6%
1120
 
0.5%
716
 
0.4%
915
 
0.3%
Other values (9)59
 
1.3%
ValueCountFrequency (%)
03894
88.0%
1107
 
2.4%
292
 
2.1%
349
 
1.1%
478
 
1.8%
568
 
1.5%
626
 
0.6%
716
 
0.4%
812
 
0.3%
915
 
0.3%
ValueCountFrequency (%)
191
 
< 0.1%
182
 
< 0.1%
162
 
< 0.1%
152
 
< 0.1%
144
 
0.1%
139
0.2%
1214
0.3%
1120
0.5%
1013
0.3%
915
0.3%

Enrolled Units (2nd Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2321429
Minimum0
Maximum23
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:07.064279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median6
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1959508
Coefficient of variation (CV)0.35235886
Kurtosis7.13474
Mean6.2321429
Median Absolute Deviation (MAD)1
Skewness0.7881135
Sum27571
Variance4.8221997
MonotonicityNot monotonic
2025-12-11T14:48:07.113433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
61913
43.2%
51054
23.8%
8661
 
14.9%
7304
 
6.9%
0180
 
4.1%
1160
 
1.4%
950
 
1.1%
1048
 
1.1%
1244
 
1.0%
1337
 
0.8%
Other values (12)73
 
1.7%
ValueCountFrequency (%)
0180
 
4.1%
13
 
0.1%
25
 
0.1%
33
 
0.1%
417
 
0.4%
51054
23.8%
61913
43.2%
7304
 
6.9%
8661
 
14.9%
950
 
1.1%
ValueCountFrequency (%)
232
 
< 0.1%
211
 
< 0.1%
193
 
0.1%
182
 
< 0.1%
1712
 
0.3%
161
 
< 0.1%
152
 
< 0.1%
1422
0.5%
1337
0.8%
1244
1.0%

Evaluated Units (2nd Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0632911
Minimum0
Maximum33
Zeros401
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:07.163233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9479509
Coefficient of variation (CV)0.48962029
Kurtosis2.0682859
Mean8.0632911
Median Absolute Deviation (MAD)2
Skewness0.33649718
Sum35672
Variance15.586317
MonotonicityNot monotonic
2025-12-11T14:48:07.222810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8792
17.9%
6614
13.9%
7563
12.7%
9456
10.3%
0401
9.1%
10355
8.0%
5288
 
6.5%
11255
 
5.8%
12226
 
5.1%
13126
 
2.8%
Other values (20)348
7.9%
ValueCountFrequency (%)
0401
9.1%
13
 
0.1%
24
 
0.1%
32
 
< 0.1%
410
 
0.2%
5288
 
6.5%
6614
13.9%
7563
12.7%
8792
17.9%
9456
10.3%
ValueCountFrequency (%)
331
 
< 0.1%
281
 
< 0.1%
272
 
< 0.1%
263
 
0.1%
251
 
< 0.1%
243
 
0.1%
234
 
0.1%
2210
0.2%
2110
0.2%
208
0.2%

Approved Units (2nd Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4358047
Minimum0
Maximum20
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:07.272731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0147639
Coefficient of variation (CV)0.67964306
Kurtosis0.84504466
Mean4.4358047
Median Absolute Deviation (MAD)2
Skewness0.30627938
Sum19624
Variance9.0888014
MonotonicityNot monotonic
2025-12-11T14:48:07.325615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6965
21.8%
0870
19.7%
5726
16.4%
4414
9.4%
7331
 
7.5%
8321
 
7.3%
3285
 
6.4%
2198
 
4.5%
1114
 
2.6%
1148
 
1.1%
Other values (10)152
 
3.4%
ValueCountFrequency (%)
0870
19.7%
1114
 
2.6%
2198
 
4.5%
3285
 
6.4%
4414
9.4%
5726
16.4%
6965
21.8%
7331
 
7.5%
8321
 
7.3%
936
 
0.8%
ValueCountFrequency (%)
202
 
< 0.1%
193
 
0.1%
182
 
< 0.1%
178
 
0.2%
162
 
< 0.1%
146
 
0.1%
1321
0.5%
1234
0.8%
1148
1.1%
1038
0.9%

Average Grade (2nd Sem)
Real number (ℝ)

High correlation  Zeros 

Distinct786
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.230206
Minimum0
Maximum18.571429
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:07.387573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median12.2
Q313.333333
95-th percentile14.980262
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.5833333

Descriptive statistics

Standard deviation5.210808
Coefficient of variation (CV)0.50935515
Kurtosis0.066567351
Mean10.230206
Median Absolute Deviation (MAD)1.2
Skewness-1.3136502
Sum45258.43
Variance27.15252
MonotonicityNot monotonic
2025-12-11T14:48:07.453176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0870
 
19.7%
12170
 
3.8%
11165
 
3.7%
13150
 
3.4%
11.586
 
1.9%
12.584
 
1.9%
1477
 
1.7%
1077
 
1.7%
13.565
 
1.5%
12.6666666761
 
1.4%
Other values (776)2619
59.2%
ValueCountFrequency (%)
0870
19.7%
1077
 
1.7%
10.166666674
 
0.1%
10.24
 
0.1%
10.2510
 
0.2%
10.3333333319
 
0.4%
10.3751
 
< 0.1%
10.48
 
0.2%
10.428571432
 
< 0.1%
10.444444442
 
< 0.1%
ValueCountFrequency (%)
18.571428571
< 0.1%
17.714285711
< 0.1%
17.692307691
< 0.1%
17.62
< 0.1%
17.58751
< 0.1%
17.428571431
< 0.1%
17.166666671
< 0.1%
172
< 0.1%
16.909090911
< 0.1%
16.82
< 0.1%

Not Evaluated Units (2nd Sem)
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15031646
Minimum0
Maximum12
Zeros4142
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:07.513441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75377407
Coefficient of variation (CV)5.0145812
Kurtosis66.811692
Mean0.15031646
Median Absolute Deviation (MAD)0
Skewness7.2677009
Sum665
Variance0.56817535
MonotonicityNot monotonic
2025-12-11T14:48:07.557901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04142
93.6%
1140
 
3.2%
248
 
1.1%
335
 
0.8%
421
 
0.5%
517
 
0.4%
68
 
0.2%
86
 
0.1%
75
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
04142
93.6%
1140
 
3.2%
248
 
1.1%
335
 
0.8%
421
 
0.5%
517
 
0.4%
68
 
0.2%
75
 
0.1%
86
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
86
 
0.1%
75
 
0.1%
68
 
0.2%
517
 
0.4%
421
 
0.5%
335
 
0.8%
248
 
1.1%
1140
 
3.2%
04142
93.6%

Unemployment Rate (%)
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.566139
Minimum7.6
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-12-11T14:48:07.599315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q19.4
median11.1
Q313.9
95-th percentile16.2
Maximum16.2
Range8.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.6638505
Coefficient of variation (CV)0.23031458
Kurtosis-0.99552591
Mean11.566139
Median Absolute Deviation (MAD)1.7
Skewness0.21205105
Sum51168.6
Variance7.0960994
MonotonicityNot monotonic
2025-12-11T14:48:07.641492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.6571
12.9%
9.4533
12.0%
10.8525
11.9%
12.4445
10.1%
12.7419
9.5%
11.1414
9.4%
15.5397
9.0%
13.9390
8.8%
8.9368
8.3%
16.2362
8.2%
ValueCountFrequency (%)
7.6571
12.9%
8.9368
8.3%
9.4533
12.0%
10.8525
11.9%
11.1414
9.4%
12.4445
10.1%
12.7419
9.5%
13.9390
8.8%
15.5397
9.0%
16.2362
8.2%
ValueCountFrequency (%)
16.2362
8.2%
15.5397
9.0%
13.9390
8.8%
12.7419
9.5%
12.4445
10.1%
11.1414
9.4%
10.8525
11.9%
9.4533
12.0%
8.9368
8.3%
7.6571
12.9%

Inflation Rate (%)
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2280289
Minimum-0.8
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)20.9%
Memory size34.7 KiB
2025-12-11T14:48:07.682723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile-0.8
Q10.3
median1.4
Q32.6
95-th percentile3.7
Maximum3.7
Range4.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.3827107
Coefficient of variation (CV)1.1259594
Kurtosis-1.0390334
Mean1.2280289
Median Absolute Deviation (MAD)1.2
Skewness0.25237535
Sum5432.8
Variance1.9118889
MonotonicityNot monotonic
2025-12-11T14:48:07.727306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.4893
20.2%
2.6571
12.9%
-0.8533
12.0%
0.5445
10.1%
3.7419
9.5%
0.6414
9.4%
2.8397
9.0%
-0.3390
8.8%
0.3362
8.2%
ValueCountFrequency (%)
-0.8533
12.0%
-0.3390
8.8%
0.3362
8.2%
0.5445
10.1%
0.6414
9.4%
1.4893
20.2%
2.6571
12.9%
2.8397
9.0%
3.7419
9.5%
ValueCountFrequency (%)
3.7419
9.5%
2.8397
9.0%
2.6571
12.9%
1.4893
20.2%
0.6414
9.4%
0.5445
10.1%
0.3362
8.2%
-0.3390
8.8%
-0.8533
12.0%

GDP per Capita (USD)
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019688065
Minimum-4.06
Maximum3.51
Zeros0
Zeros (%)0.0%
Negative1711
Negative (%)38.7%
Memory size34.7 KiB
2025-12-11T14:48:07.771169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.06
5-th percentile-4.06
Q1-1.7
median0.32
Q31.79
95-th percentile3.51
Maximum3.51
Range7.57
Interquartile range (IQR)3.49

Descriptive statistics

Standard deviation2.2699354
Coefficient of variation (CV)1152.95
Kurtosis-1.0016532
Mean0.0019688065
Median Absolute Deviation (MAD)1.47
Skewness-0.39406821
Sum8.71
Variance5.1526069
MonotonicityNot monotonic
2025-12-11T14:48:07.955172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.32571
12.9%
-3.12533
12.0%
1.74525
11.9%
1.79445
10.1%
-1.7419
9.5%
2.02414
9.4%
-4.06397
9.0%
0.79390
8.8%
3.51368
8.3%
-0.92362
8.2%
ValueCountFrequency (%)
-4.06397
9.0%
-3.12533
12.0%
-1.7419
9.5%
-0.92362
8.2%
0.32571
12.9%
0.79390
8.8%
1.74525
11.9%
1.79445
10.1%
2.02414
9.4%
3.51368
8.3%
ValueCountFrequency (%)
3.51368
8.3%
2.02414
9.4%
1.79445
10.1%
1.74525
11.9%
0.79390
8.8%
0.32571
12.9%
-0.92362
8.2%
-1.7419
9.5%
-3.12533
12.0%
-4.06397
9.0%

Student Status
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Graduate
2209 
Dropout
1421 
Enrolled
794 

Length

Max length8
Median length8
Mean length7.6787975
Min length7

Characters and Unicode

Total characters33971
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDropout
2nd rowGraduate
3rd rowDropout
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate2209
49.9%
Dropout1421
32.1%
Enrolled794
 
17.9%

Length

2025-12-11T14:48:08.009988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:48:08.050195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate2209
49.9%
dropout1421
32.1%
enrolled794
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
t3630
10.7%
u3630
10.7%
e3003
8.8%
d3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)33971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
t3630
10.7%
u3630
10.7%
e3003
8.8%
d3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)33971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
t3630
10.7%
u3630
10.7%
e3003
8.8%
d3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)33971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
t3630
10.7%
u3630
10.7%
e3003
8.8%
d3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Interactions

2025-12-11T14:48:03.261821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:46.805356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.819372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.797688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.883112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.877017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.845711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.838678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.013808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.018037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.056100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.011945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.015693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.106819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.173632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.366069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.310741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.318831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:46.874040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.873273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.865258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.945958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.934164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.897962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.902818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.072536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.077662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.108117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.068264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.082650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.167295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.226790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.416247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.369668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.377007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:46.932791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.925362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.927291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.003326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.987413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.953658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.960121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.127751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.139199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.161974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.126232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.145741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.232902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.285599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.470272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.422845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.448542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.003267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.988004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.994929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.064173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.052838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.015381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.025421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.187505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.200884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.227120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.187688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.211457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.298302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.358548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.528155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.485040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.505486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.083377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.042204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.055483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.118108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.105340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.072418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.090588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.244726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.263063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.278996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.245927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.272792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.358448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.417769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.582467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.538369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.561853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.134992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.096947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.118518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.173021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.158090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.134476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.147737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.297724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.320755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.335222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.300163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.333482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.416716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.475237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.639687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.592382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.627060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.187465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.156342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.180953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.228847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.211377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.188286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.207510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.354838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.376681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.388720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.360991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.395430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.476910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.532674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.694367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.646333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.686729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.245514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.214299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.249628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.294482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.270074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.249626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.273986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.419985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.438360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.447078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.431412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.464907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.541353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.595486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.755124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.708814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.745755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.300757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.272805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.313704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.352508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.325694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.307399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.336200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.475780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.505745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.503490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.486205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.529555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.614098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.653002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.809238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.762626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.814385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.357939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.331717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.377243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.410046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.392105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.368528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.400090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.535670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.568012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.560828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.548031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.590441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.672412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.722632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.866446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.819864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.868983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.412258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.386615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.438598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.466283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.443146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.423263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.457955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.591209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.628324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.618651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.603983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.656058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.727975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.790082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.917276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.870102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.928275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.469751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.443506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.501759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.522715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.498053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.479941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.521319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.666851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.688448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.671552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.660789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.719774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.788304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.861967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.980607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.922731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.990485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.532062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.508927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.568975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.585273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.560272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.548700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.590566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.724444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.752299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.731703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.728060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.784116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.860968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.930073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.040352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.984409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:04.051201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.591194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.569748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.634544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.652662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.619227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.609348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.777134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.789456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.813474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.789481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.788653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.856210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.920687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.994595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.097092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.043187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:04.109290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.648698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.628947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.700157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.710453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.674908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.667570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.836421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.846962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.878030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.846054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.848791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.921407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.991996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.052055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.155038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.104273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:04.172415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.701739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.682985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.758692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.763152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.729317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.723416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.894810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.903273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.935946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.898408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.903760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:58.983246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.053905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.103506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.203100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.158691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:04.227202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:47.755984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:48.739983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:49.819156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:50.817433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:51.787324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:52.779104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:53.953711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:54.957989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:55.993976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:56.957705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:57.957321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:47:59.043707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:00.111126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:01.306945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:02.251833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:48:03.207699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-11T14:48:08.112905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age at EnrollmentApplication ModeApplication OrderApproved Units (1st Sem)Approved Units (2nd Sem)Average Grade (1st Sem)Average Grade (2nd Sem)Course NameCredited Units (1st Sem)Credited Units (2nd Sem)Daytime/Evening AttendanceDisplaced StudentEnrolled Units (1st Sem)Enrolled Units (2nd Sem)Evaluated Units (1st Sem)Evaluated Units (2nd Sem)Father's OccupationFather's QualificationGDP per Capita (USD)Gender (1=Male, 0=Female)Inflation Rate (%)International StudentIs DebtorMarital StatusMother's OccupationMother's QualificationNationalityNot Evaluated Units (1st Sem)Not Evaluated Units (2nd Sem)Previous QualificationScholarship HolderSpecial Educational NeedsStudent StatusTuition Fees Up-to-DateUnemployment Rate (%)
Age at Enrollment1.0000.288-0.366-0.166-0.188-0.210-0.2130.1890.2990.3010.4890.3890.001-0.0310.1710.0830.0620.133-0.0570.1620.0180.0000.1330.3130.0840.1490.0000.0610.0940.1950.2120.0000.2190.2090.019
Application Mode0.2881.0000.1610.1870.2890.1110.1100.1680.2670.2320.4100.4110.1980.2000.1430.1280.0770.0940.1620.1770.1490.5300.1640.2240.1060.0880.2340.0580.0370.4120.2200.0000.2210.2020.176
Application Order-0.3660.1611.0000.0900.1060.0740.0650.143-0.202-0.2010.1850.3820.0560.075-0.096-0.0530.0000.0380.0300.108-0.0110.0000.0900.0700.0000.0330.000-0.041-0.0300.0860.0890.0000.0790.073-0.104
Approved Units (1st Sem)-0.1660.1870.0901.0000.8920.6400.6630.2360.3620.3670.1730.1290.7070.6990.2620.3640.0000.0420.0590.237-0.0020.0000.1450.0510.0560.0980.000-0.069-0.0730.1280.2640.0000.4550.2800.074
Approved Units (2nd Sem)-0.1880.2890.1060.8921.0000.6290.6940.2880.2890.3190.1010.1160.6530.6740.2520.3030.0240.0350.0480.262-0.0150.0250.1800.0480.0580.0920.000-0.055-0.0640.1180.2730.0000.5160.3130.070
Average Grade (1st Sem)-0.2100.1110.0740.6400.6291.0000.7620.2710.1010.0970.1380.0870.3630.3680.1120.1820.0530.0520.0920.193-0.0370.0000.1060.0390.0570.0650.026-0.017-0.0470.0930.1810.0000.3830.2480.045
Average Grade (2nd Sem)-0.2130.1100.0650.6630.6940.7621.0000.2280.0930.1030.0850.0690.3510.3640.0900.1720.0750.0490.1050.201-0.0430.0000.1500.0330.0870.0600.067-0.041-0.0520.1040.1960.0000.4550.2970.042
Course Name0.1890.1680.1430.2360.2880.2710.2281.0000.1170.1160.9980.3240.4340.4370.2410.2450.0890.0870.1200.4240.0900.0930.1520.1670.0890.0960.0400.1010.0820.1110.2220.0620.2440.1390.123
Credited Units (1st Sem)0.2990.267-0.2020.3620.2890.1010.0930.1171.0000.9140.1710.1070.4230.3760.3660.3310.0230.0710.0220.027-0.0020.0000.0570.0500.0380.0870.0000.1000.0640.1830.0860.0000.0450.0000.024
Credited Units (2nd Sem)0.3010.232-0.2010.3670.3190.0970.1030.1160.9141.0000.1820.1280.4400.4140.3690.3420.0000.0340.0240.028-0.0010.0000.0540.0470.0270.1210.0000.0590.0820.1760.0750.0000.0420.0000.012
Daytime/Evening Attendance0.4890.4100.1850.1730.1010.1380.0850.9980.1710.1821.0000.2510.2190.1870.0630.1080.1260.2400.0930.0000.0730.0210.0000.3660.1720.2740.0000.0190.0000.1950.0920.0230.0780.0350.093
Displaced Student0.3890.4110.3820.1290.1160.0870.0690.3240.1070.1280.2511.0000.1500.1380.0980.0680.1120.1150.1400.1240.0580.0000.0880.2750.1020.1440.0290.0000.0360.2210.0710.0000.1120.0940.138
Enrolled Units (1st Sem)0.0010.1980.0560.7070.6530.3630.3510.4340.4230.4400.2190.1501.0000.9620.4200.4310.0030.0490.0180.2130.0170.0000.0470.0670.0340.0820.000-0.019-0.0210.1360.1560.0430.1730.0830.108
Enrolled Units (2nd Sem)-0.0310.2000.0750.6990.6740.3680.3640.4370.3760.4140.1870.1380.9621.0000.3860.4400.0570.0370.0190.1620.0090.0300.0750.0340.0370.1220.000-0.032-0.0270.1080.1130.0000.1390.1240.139
Evaluated Units (1st Sem)0.1710.143-0.0960.2620.2520.1120.0900.2410.3660.3690.0630.0980.4200.3861.0000.6940.0000.077-0.0970.079-0.0400.0560.0610.0320.0000.0320.1690.2020.1530.1030.1870.0000.2580.1030.067
Evaluated Units (2nd Sem)0.0830.128-0.0530.3640.3030.1820.1720.2450.3310.3420.1080.0680.4310.4400.6941.0000.0000.000-0.0040.109-0.0240.0110.0590.0150.0000.0040.0800.0960.1590.1140.1660.0290.2740.1240.061
Father's Occupation0.0620.0770.0000.0000.0240.0530.0750.0890.0230.0000.1260.1120.0030.0570.0000.0001.0000.1820.1610.0890.1270.0600.1380.0000.5710.1890.1160.0000.0000.0500.2250.0000.1400.0190.174
Father's Qualification0.1330.0940.0380.0420.0350.0520.0490.0870.0710.0340.2400.1150.0490.0370.0770.0000.1821.0000.1210.0950.1090.0770.0000.0980.1540.4310.0510.0970.1470.0980.1700.0000.1340.0970.135
GDP per Capita (USD)-0.0570.1620.0300.0590.0480.0920.1050.1200.0220.0240.0930.1400.0180.019-0.097-0.0040.1610.1211.0000.081-0.1020.0650.1380.0380.1630.1410.041-0.184-0.1110.1390.1290.0570.0520.079-0.288
Gender (1=Male, 0=Female)0.1620.1770.1080.2370.2620.1930.2010.4240.0270.0280.0000.1240.2130.1620.0790.1090.0890.0950.0811.0000.0680.0200.0510.0440.0380.0830.0290.0000.0550.1240.1680.0030.2290.1020.076
Inflation Rate (%)0.0180.149-0.011-0.002-0.015-0.037-0.0430.090-0.002-0.0010.0730.0580.0170.009-0.040-0.0240.1270.109-0.1020.0681.0000.0300.0860.0400.1290.1220.017-0.068-0.0270.1270.0980.0420.0360.086-0.055
International Student0.0000.5300.0000.0000.0250.0000.0000.0930.0000.0000.0210.0000.0000.0300.0560.0110.0600.0770.0650.0200.0301.0000.0720.0000.1720.1010.9980.0570.0000.0000.0220.0000.0000.0390.069
Is Debtor0.1330.1640.0900.1450.1800.1060.1500.1520.0570.0540.0000.0880.0470.0750.0610.0590.1380.0000.1380.0510.0860.0721.0000.0300.1170.0350.0900.0310.0630.1440.0650.0000.2410.4070.135
Marital Status0.3130.2240.0700.0510.0480.0390.0330.1670.0500.0470.3660.2750.0670.0340.0320.0150.0000.0980.0380.0440.0400.0000.0301.0000.0570.1340.0000.0550.0440.1550.1040.0000.0780.0930.044
Mother's Occupation0.0840.1060.0000.0560.0580.0570.0870.0890.0380.0270.1720.1020.0340.0370.0000.0000.5710.1540.1630.0380.1290.1720.1170.0571.0000.2490.2150.0520.0000.0630.1950.0670.1610.0950.176
Mother's Qualification0.1490.0880.0330.0980.0920.0650.0600.0960.0870.1210.2740.1440.0820.1220.0320.0040.1890.4310.1410.0830.1220.1010.0350.1340.2491.0000.0000.0650.0990.0910.1820.0230.1350.0560.156
Nationality0.0000.2340.0000.0000.0000.0260.0670.0400.0000.0000.0000.0290.0000.0000.1690.0800.1160.0510.0410.0290.0170.9980.0900.0000.2150.0001.0000.1680.0000.0000.0310.0000.0260.0490.041
Not Evaluated Units (1st Sem)0.0610.058-0.041-0.069-0.055-0.017-0.0410.1010.1000.0590.0190.000-0.019-0.0320.2020.0960.0000.097-0.1840.000-0.0680.0570.0310.0550.0520.0650.1681.0000.3840.0760.0570.0000.0620.078-0.067
Not Evaluated Units (2nd Sem)0.0940.037-0.030-0.073-0.064-0.047-0.0520.0820.0640.0820.0000.036-0.021-0.0270.1530.1590.0000.147-0.1110.055-0.0270.0000.0630.0440.0000.0990.0000.3841.0000.0950.0400.0000.0660.071-0.053
Previous Qualification0.1950.4120.0860.1280.1180.0930.1040.1110.1830.1760.1950.2210.1360.1080.1030.1140.0500.0980.1390.1240.1270.0000.1440.1550.0630.0910.0000.0760.0951.0000.1390.0000.1460.1540.153
Scholarship Holder0.2120.2200.0890.2640.2730.1810.1960.2220.0860.0750.0920.0710.1560.1130.1870.1660.2250.1700.1290.1680.0980.0220.0650.1040.1950.1820.0310.0570.0400.1391.0000.0110.3040.1360.130
Special Educational Needs0.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0230.0000.0430.0000.0000.0290.0000.0000.0570.0030.0420.0000.0000.0000.0670.0230.0000.0000.0000.0000.0111.0000.0000.0000.048
Student Status0.2190.2210.0790.4550.5160.3830.4550.2440.0450.0420.0780.1120.1730.1390.2580.2740.1400.1340.0520.2290.0360.0000.2410.0780.1610.1350.0260.0620.0660.1460.3040.0001.0000.4310.053
Tuition Fees Up-to-Date0.2090.2020.0730.2800.3130.2480.2970.1390.0000.0000.0350.0940.0830.1240.1030.1240.0190.0970.0790.1020.0860.0390.4070.0930.0950.0560.0490.0780.0710.1540.1360.0000.4311.0000.091
Unemployment Rate (%)0.0190.176-0.1040.0740.0700.0450.0420.1230.0240.0120.0930.1380.1080.1390.0670.0610.1740.135-0.2880.076-0.0550.0690.1350.0440.1760.1560.041-0.067-0.0530.1530.1300.0480.0530.0911.000

Missing values

2025-12-11T14:48:04.384357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-11T14:48:04.568436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Marital StatusApplication ModeApplication OrderCourse NameDaytime/Evening AttendancePrevious QualificationNationalityMother's QualificationFather's QualificationMother's OccupationFather's OccupationDisplaced StudentSpecial Educational NeedsIs DebtorTuition Fees Up-to-DateGender (1=Male, 0=Female)Scholarship HolderAge at EnrollmentInternational StudentCredited Units (1st Sem)Enrolled Units (1st Sem)Evaluated Units (1st Sem)Approved Units (1st Sem)Average Grade (1st Sem)Not Evaluated Units (1st Sem)Credited Units (2nd Sem)Enrolled Units (2nd Sem)Evaluated Units (2nd Sem)Approved Units (2nd Sem)Average Grade (2nd Sem)Not Evaluated Units (2nd Sem)Unemployment Rate (%)Inflation Rate (%)GDP per Capita (USD)Student Status
0Single2nd phase—general contingent5Animation and Multimedia DesignDaytimeSecondary educationPortugueseGeneral commerce courseOther—11th YearService/Sales/SecurityUnskilled WorkersYesNoNoYesMaleNo20No00000.000000000000.000000010.81.41.74Dropout
1SingleInternational student (bachelor)1TourismDaytimeSecondary educationPortugueseSecondary EducationHigher Education DegreeTechnicians & ProfessionalsTechnicians & ProfessionalsYesNoNoNoMaleNo19No066614.0000000066613.666667013.9-0.30.79Graduate
2Single1st phase—general contingent5Communication DesignDaytimeSecondary educationPortugueseAdministration & Commerce courseBasic Edu 1st CycleUnskilled WorkersUnskilled WorkersYesNoNoNoMaleNo19No06000.000000006000.000000010.81.41.74Dropout
3Single2nd phase—general contingent2Journalism and CommunicationDaytimeSecondary educationPortugueseAccounting & Admin courseBasic Edu 1st CycleService/Sales/SecurityTechnicians & ProfessionalsYesNoNoYesFemaleNo20No068613.42857100610512.40000009.4-0.8-3.12Graduate
4MarriedOver 23 years old1Social Service (evening attendance)EveningSecondary educationPortugueseAdministration & Commerce courseBasic Edu 2nd CycleUnskilled WorkersUnskilled WorkersNoNoNoYesFemaleNo45No069512.3333330066613.000000013.9-0.30.79Graduate
5MarriedOver 23 years old1Management (evening attendance)EveningBasic education 3rd cyclePortugueseAdministration & Commerce courseBasic Edu 1st CycleUnskilled WorkersSkilled Industry WorkersNoNoYesYesMaleNo50No0510511.85714300517511.500000516.20.3-0.92Graduate
6Single1st phase—general contingent1NursingDaytimeSecondary educationPortugueseGeneral commerce courseBasic Edu 2nd CycleSkilled Industry WorkersArmed ForcesYesNoNoYesFemaleYes18No079713.3000000088814.345000015.52.8-4.06Graduate
7Single3rd phase—general contingent4TourismDaytimeSecondary educationPortugueseAdministration & Commerce courseBasic Edu 1st CycleUnskilled WorkersUnskilled WorkersYesNoNoNoMaleNo22No05500.000000005500.000000015.52.8-4.06Dropout
8Single1st phase—general contingent3Social ServiceDaytimeSecondary educationRomanianSecondary EducationSecondary EducationUnskilled WorkersUnskilled WorkersNoNoNoYesFemaleYes21Yes068613.8750000067614.142857016.20.3-0.92Graduate
9Single1st phase—general contingent1Social ServiceDaytimeSecondary educationPortugueseSecondary EducationBasic Education 3rd CycleAdministrative StaffSkilled Industry WorkersYesNoYesNoFemaleNo18No069511.40000000614213.50000008.91.43.51Dropout
Marital StatusApplication ModeApplication OrderCourse NameDaytime/Evening AttendancePrevious QualificationNationalityMother's QualificationFather's QualificationMother's OccupationFather's OccupationDisplaced StudentSpecial Educational NeedsIs DebtorTuition Fees Up-to-DateGender (1=Male, 0=Female)Scholarship HolderAge at EnrollmentInternational StudentCredited Units (1st Sem)Enrolled Units (1st Sem)Evaluated Units (1st Sem)Approved Units (1st Sem)Average Grade (1st Sem)Not Evaluated Units (1st Sem)Credited Units (2nd Sem)Enrolled Units (2nd Sem)Evaluated Units (2nd Sem)Approved Units (2nd Sem)Average Grade (2nd Sem)Not Evaluated Units (2nd Sem)Unemployment Rate (%)Inflation Rate (%)GDP per Capita (USD)Student Status
4414Single1st phase—general contingent1EquinicultureDaytimeSecondary educationPortugueseHigher Education DegreeBasic Edu 2nd CycleTechnicians & ProfessionalsService/Sales/SecurityYesNoNoYesFemaleNo18No056511.8000001058511.60000009.4-0.8-3.12Graduate
4415DivorcedOver 23 years old1NursingDaytimeBasic education 3rd cyclePortugueseAdministration & Commerce courseBasic Edu 1st CycleFarmers/Ag WorkersFarmers/Ag WorkersNoNoYesNoFemaleNo46No0714312.33333300712311.083333011.10.62.02Dropout
4416SingleChange in course2NursingDaytimeSecondary educationPortugueseAccounting & Admin courseBasic Edu 2nd CycleUnskilled WorkersService/Sales/SecurityNoNoNoYesFemaleNo23No1114151212.62500011114151212.62500017.62.60.32Graduate
4417Single1st phase—general contingent1Communication DesignDaytimeSecondary educationPortugueseSecondary EducationSecondary EducationUnskilled WorkersUnskilled WorkersYesNoNoYesFemaleYes20No066613.8333330066613.500000016.20.3-0.92Graduate
4418SingleTechnological specialization diploma holders1Communication DesignDaytimeTechnological specialization coursePortugueseHigher Education DegreeBasic Edu 2nd CycleTechnicians & ProfessionalsUnskilled WorkersNoNoNoYesMaleNo20No277612.50000005910713.142857116.20.3-0.92Graduate
4419Single1st phase—general contingent6Journalism and CommunicationDaytimeSecondary educationPortugueseSecondary EducationSecondary EducationService/Sales/SecurityAdministrative StaffNoNoNoYesMaleNo19No067513.6000000068512.666667015.52.8-4.06Graduate
4420Single1st phase—general contingent2Journalism and CommunicationDaytimeSecondary educationRussianSecondary EducationSecondary EducationUnskilled WorkersUnskilled WorkersYesNoYesNoFemaleNo18Yes066612.0000000066211.000000011.10.62.02Dropout
4421Single1st phase—general contingent1NursingDaytimeSecondary educationPortugueseAdministration & Commerce courseBasic Edu 1st CycleUnskilled WorkersUnskilled WorkersYesNoNoYesFemaleYes30No078714.9125000089113.500000013.9-0.30.79Dropout
4422Single1st phase—general contingent1ManagementDaytimeSecondary educationPortugueseAdministration & Commerce courseBasic Edu 1st CycleSkilled Industry WorkersAdministrative StaffYesNoNoYesFemaleYes20No055513.8000000056512.00000009.4-0.8-3.12Graduate
4423SingleOrdinance No. 854-B/991Journalism and CommunicationDaytimeSecondary educationCape VerdeanAccounting & Admin courseBasic Edu 1st CycleService/Sales/SecurityUnskilled WorkersYesNoNoYesFemaleNo22Yes068611.6666670066613.000000012.73.7-1.70Graduate